Phyllis Wan

Welcome!

I am an Assistant Professor in Statistics at the Erasmus School of Economics, Erasmus University Rotterdam.

My research interest lies in the study of complex data structures, such as networks and time series. I am especially interested in scenarios where heavy-tailed observations are present and where novel statistical tools are called for!

I received my PhD from the Department of Statistics at Columbia University, under the supervision of Prof. Richard Davis.

Contact

Erasmus School of Economics, E T-45

Erasmus University Rotterdam

P.O. Box 1738

3000DR Rotterdam, the Netherlands

wan at ese.eur.nl

Papers

  • Parametric and non-parametric estimation of extreme earthquake events: the joint tail inference for mainshocks and aftershocks (2019+), with Cai, J.-J. and Ozel, G. [arXiv]
  • K-means clustering of extremes (2019+), with Jan├čen, A. [arXiv]
  • Goodness-of-fit testing for time series models via distance covariance (2019+), with Davis, R.A. [arXiv]
  • Forecasting the urban skyline with extreme value analysis (2019+), with Auerbach, J.L. [arXiv]
  • Are extreme value estimation methods useful for network data? (2019+), with Wang, T., Davis, R.A. and Resnick, S.I., in Extremes. [link, arXiv]
  • Threshold selection for multivariate heavy-tailed data (2019), with Davis, R.A., in Extremes. [link, arXiv]
  • Applications of distance covariance to time series (2018), with Davis, R.A., Matsui, M. and Mikosch, T., in Bernoulli. [link, arXiv]
  • Fitting the linear preferential attachment model (2017), with Wang, T., Davis, R.A. and Resnick, S.I., in Electronic Journal of Statistics. [link, arXiv]
  • Nonstandard regular variation of in-degree and out-degree in the preferential attachment model (2016), with Samorodnitsky, G., Resnick, S.I., Towsley, D., Davis, R.A. and Willis, A., in Journal of Applied Probability. [link, arXiv]

Google Scholar profile

List of presentations

Teaching

  • @Erasmus
    • FEB12005X: Applied Statistics 2
    • FEB23015: Seminar in Business Analytics and Quantitative Marketing
  • @Columbia
    • STAT 1111: Introduction to Statistics
    • Review for Theoretical Statistics PhD Qualification Exam